---
title: "Analyzing the Operation Strategies for Seattle Airbnb Hosts and Potential Hosts"
author: Siqi Zhang
output:
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    source: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
```

```{r warning=FALSE, message=FALSE}
#install.packages("maps")
# load packages
library(leaflet)
library(maps)
library(tidyverse)
library(DMwR)
library(microplot)
library(scales)
library(plotly)
```


```{r warning=FALSE}
# Load data
sea_airbnb <-read.csv("listings.csv")

# Clean data
# change column names
names(sea_airbnb)[9] <- "neighbourhood"
names(sea_airbnb)[10] <- "neighbourhood_group"

# delete NAs and unused levels
sea_airbnb <- sea_airbnb[complete.cases(sea_airbnb), ]
sea_airbnb <- sea_airbnb[!(sea_airbnb$host_response_rate=="N/A"),]
sea_airbnb$host_response_time <- droplevels(sea_airbnb$host_response_time)
sea_airbnb$host_is_superhost <- droplevels(sea_airbnb$host_is_superhost)
sea_airbnb$host_response_rate <- droplevels(sea_airbnb$host_response_rate)

# extract year only from host_since variable
sea_airbnb$host_since <- strptime(as.character(sea_airbnb$host_since), "%m/%d/%y")
sea_airbnb$host_since <- substring(sea_airbnb$host_since, 1, 4)
sea_airbnb$host_since <- as.numeric(as.character(sea_airbnb$host_since))

# convert factor variables to numerical variables
sea_airbnb$host_response_rate <- as.numeric(sub("%", "", sea_airbnb$host_response_rate,fixed=TRUE))/100
sea_airbnb$price <- as.numeric(sub("$", "", sea_airbnb$price,fixed=TRUE))

# clean NA in price variable
sea_airbnb <- sea_airbnb[complete.cases(sea_airbnb), ]

# add weighted review score column
sea_airbnb$year_weight <- SoftMax(sea_airbnb$host_since)
sea_airbnb$review_weight <- log(sea_airbnb$number_of_reviews)
sea_airbnb$weighted_score <- sea_airbnb$review_scores_rating*sea_airbnb$year_weight*sea_airbnb$review_weight

# convert numeric weighted review score data to categorical performance
sea_airbnb$performance[sea_airbnb$weighted_score >= 0 & sea_airbnb$weighted_score <=  20]  = "Bad"
sea_airbnb$performance[sea_airbnb$weighted_score > 20 & sea_airbnb$weighted_score <=  120]  = "Poor"
sea_airbnb$performance[sea_airbnb$weighted_score > 120 & sea_airbnb$weighted_score <=  300]  = "Fair"
sea_airbnb$performance[sea_airbnb$weighted_score > 300 & sea_airbnb$weighted_score <=  400]  = "Good"
sea_airbnb$performance[sea_airbnb$weighted_score > 400]  = "Excellent"
sea_airbnb$performance = factor(sea_airbnb$performance, levels=c("Bad", "Poor", "Fair", "Good", "Excellent"))

# change level names for variables
levels(sea_airbnb$host_is_superhost) <- c("No", "Yes")
levels(sea_airbnb$instant_bookable) <- c("No", "Yes")

# adjusted columns for analysis use
sea_airbnb <- select(sea_airbnb, -year_weight, -review_weight)

# mutate annual_revenue column
# for simplicity, I assume reviews per month will be the days Airbnb booked every month.And I used monthly review numbers*12 to get the yearly booking days.
sea_airbnb$annual_rev <- sea_airbnb$price*sea_airbnb$reviews_per_month*12
```

### Distribution of Airbnb in Seattle and Brief introduction of Analysis
```{r}
leaflet(sea_airbnb) %>% 
  addTiles() %>%
  addCircles(lng = ~longitude, lat = ~latitude)
```

***

**Distribution of Seattle Airbnb**

- Beltown, Broadway, First Hill have the highest density of Airbnb.

**Introduction of Seattle Airbnb Analysis**

- **`r dim(sea_airbnb)[1]` observations** of **`r dim(sea_airbnb)[2]` variables**
- focus on whether the **host is superhost**, whether the home is **instant bookable**, **neighbourhood group(location)**, number of **reviews per month**, **performance**, **price** and **annual revenue**.
    
      - **Performance** is based on total review numbers, host years, and review scores, which reflects the overall operation facts.
      - **Reviews per month** is used to measure the popularity of each Airbnb home.


### Which is the best location for Airbnb host??

```{r}
ggplotly(sea_airbnb %>%
  group_by(neighbourhood_group) %>%
  summarise(med_price = median(price)) %>%
  ggplot(mapping = aes(x = neighbourhood_group, y = med_price)) +
  stat_summary(fun.y=median,geom="line",lwd=0.6,aes(group=1)) +
  coord_flip() +
  ggtitle("Which Location Have High Home Price??") +
  labs(x = "Neighbourhood Group", y = "Median Price") +
  geom_vline(xintercept = 13, linetype = 2, color = "red", alpha = 0.75) +
  geom_vline(xintercept = 7, linetype = 2, color = "red", alpha = 0.75) +
  geom_vline(xintercept = 4, linetype = 2, color = "red", alpha = 0.75) +
  theme_classic() +
  theme(plot.title = element_text(face = "bold"),
        axis.ticks.x = element_blank(),
        axis.line.x = element_blank(),
        axis.ticks.y = element_blank(),
        axis.line.y = element_blank(),
        legend.position = "none") +
  scale_y_continuous(label = dollar))
```

***

**Highest Airbnb Home Price Neighbourhood Group**

- Downtown, Queen Anne, and Cascade have the highest median price.
- Northgate, Lake City and Delridge have the lowest median price.

    - imply that Airbnb in Downtown, Queen Anne and Cascade have more chance to earn higher revenue.
    

### What Price Range is More Welcomed??

```{r}
sea_airbnb$high_monthly_reviews[sea_airbnb$reviews_per_month >= 10]  = "high"
sea_airbnb$high_monthly_reviews[sea_airbnb$reviews_per_month < 10] = "low"
sea_airbnb$high_monthly_reviews = factor(sea_airbnb$high_monthly_reviews, levels=c("low", "high"))

ggplotly(sea_airbnb %>%
  group_by(high_monthly_reviews) %>%
  ggplot(mapping = aes(x = reviews_per_month, y = price, color = high_monthly_reviews)) +
  geom_point(alpha = 0.8) +
  ggtitle("What Price Range Is More Welcomed??") +
  labs(x = "Reviews Per Month", y = "Price") +
  geom_hline(yintercept = 250, linetype = 2, color = "black") +
  #annotate("text", x = 16.5, y = 5150, label = "High revenue range", color = "black", size = 3.5)
  scale_color_manual(values=c("#999999", "red")) +
  theme_classic() +
  theme(plot.title = element_text(face = "bold"),
        axis.ticks.x = element_blank(),
        axis.line.x = element_blank(),
        axis.ticks.y = element_blank(),
        axis.line.y = element_blank(),
        legend.position = "none") +
  scale_y_continuous(label = dollar))
```

***

**How to set reasonable prices??**

- Prices of high reveiew numbers Airbnb (the red dots) are all **lower than $250**.
- Most of the data point are **clustered under the price of $250**.


### Should You Be the Superhost??

```{r}
ggplotly(sea_airbnb %>%
  group_by(performance, host_is_superhost) %>%
  summarise(med_revenue = median(annual_rev)) %>%
  ggplot(mapping = aes(x =performance, y = med_revenue, group = host_is_superhost)) +
  geom_line(aes(color = host_is_superhost)) +
  geom_point() +
  geom_hline(yintercept = 6050, linetype = 2, color = "black") +
  ggtitle("Being Superhost Can Earn More Revenue Per Year") +
  labs(x = "Performance", y = "Median Revenue", color = "Host is superhost") +
  theme_classic() +
  theme(plot.title = element_text(face = "bold"),
        axis.ticks.x = element_blank(),
        axis.text.x = element_text(face = "bold")) +
  scale_color_manual(values = c("#969696", "red"), labels = c("No", "Yes"), guide = guide_legend(reverse = TRUE)) +
  scale_y_continuous(label = dollar)) %>%
    layout(showlegend = FALSE)
```

***

**Shoule Be the Superhost**

- Median revenue of superhost is higher than non-superhost.
- If the **superhost** has the **excellent performance**, the revenue will be highest.


### Making Your Airbnb Instant Bookable!

```{r}
ggplotly(sea_airbnb %>%
  group_by(neighbourhood_group, instant_bookable) %>%
  summarise(med_revenue = median(annual_rev)) %>%
  ggplot(mapping = aes(x = neighbourhood_group, y = med_revenue, color = instant_bookable, group = instant_bookable)) +
  geom_line(aes(color = instant_bookable)) +
  geom_point() +
  ggtitle("Making Your Home Instant Bookable", subtitle = "Revenue of Instant Bookable Airbnb home is higher") +
  coord_flip() +
  labs(x = "Neighbourhood Group", y = "Median Revenue", color = "Instant Bookable") +
  annotate("text", x = 16.5, y = 5150, label = "High revenue range", color = "black", size = 3.5) +
  geom_hline(yintercept = 6200, linetype = 2, color = "black", alpha = 0.5) +
  geom_hline(yintercept = 4000, linetype = 2, color = "black", alpha = 0.5) +
  theme_classic() +
  theme(plot.title = element_text(face = "bold"),
        axis.ticks.x = element_blank(),
        axis.line.x = element_blank(),
        axis.ticks.y = element_blank(),
        axis.line.y = element_blank(),
        axis.text.x = element_text(face = "bold"),
        legend.position = "bottom") +
  scale_color_manual(values = c("#969696", "red"), labels = c("No", "Yes"), guide = guide_legend(reverse = TRUE)) +
  scale_y_continuous(label = dollar)) %>%
  layout(showlegend = FALSE)
```

***

**Instant Bookable can increase revenue**

- The median revenues of high revenue range all come from instant bookable group.
- Instant bookable Airbnb in Capital Hill can earn the highest median revenue.


### Conclusion and Recommendations

**Conclusion**: host is superhost, performance, instant bookable and neighbourhood group are closely related to price and revenue.



**Recommendations**

**Being Superhost**

- Airbnb of superhost is more popular.
- The **revenue of superhost is higher** across different performance levels and each neighbourhood group.



**Setting Resonable Price**

- Most of the booking prices are **under $250**.



**Making Your Airbnb Home Instant Bookable**

- Instant bookable **revenue distrbution is higher** than non-instant bookable Airbnbs.
- In each neighbourhood group, instant bookable Airbnb can get higher median revenus.
    


**Making Flexible or Moderate Cancellation Policies**

- **Flexible** and **moderate** cancellation policies have relatively higher median annual revenue.
    


**Choose the Right Neighbourhood Group**

- If you are still thinking host Airbnb in which area, **Downtown**, **Cascade** and **Queen Anne** will be the good choice, which have higher median price distributions.



Contact Me: **zhangsiqi@seattleu.edu**